Leveraging rear-view sensors for automatic emergency braking in autonomous machine applications

ABSTRACT

In various examples, activation criteria and/or braking profiles corresponding to automatic emergency braking (AEB) systems and/or collision mitigation warning (CMW) systems may be determined using sensor data representative of an environment to a front, side, and/or rear of a vehicle. For example, activation criteria for triggering an AEB system and/or CMW system may be adjusted by leveraging the availability of additional information with regards to the surrounding environment of a vehicle—such as the presence of a trailing vehicle. In addition, the braking profile for the AEB activation may be adjusted based on information about the presence of and/or location of vehicles to the front, rear, and/or side of the vehicle. By adjusting the activation criteria and/or braking profiles of an AEB system, the potential for collisions with dynamic objects in the environment is reduced and the overall safety of the vehicle and its passengers is increased.

BACKGROUND

Avoiding collisions with objects—such as other vehicles, pedestrians,bicyclists, and the like—is a primary focus of modern autonomous andsemi-autonomous machine applications. For example, many vehicles areequipped with automatic emergency braking (AEB) systems and/or collisionmitigation warning (CMW) systems as part of an Advanced DriverAssistance System (ADAS), and include a combination of hardware andsoftware that is used to detect a potential impending forward collisionwith another object in time to avoid or mitigate the crash. To assistwhen drivers become inattentive and/or an unpredicted situation ispresented, these AEB systems may cause the brakes of the vehicle toautomatically engage to assist in preventing or reducing the severity ofa collision. For CMW systems, a signal—e.g., audible, visual, orotherwise—may be generated to warn the driver of an impending collision.

However, conventional AEB systems are designed to avoid false positivesand thus only brake the vehicle when there is high confidence that acollision is imminent. For example, AEB systems in production mayaddress false braking using two paths that are implemented with sensordiversity (e.g., two or more of RADAR, LIDAR, SONAR, ultrasonic,cameras, etc.). As a result, where a collision is possible, but one ofthe determinations is not to brake, the AEB system may not activateresulting in a false negative event and a collision may ensue. BecauseAEB systems are considered a driver assistance feature, for any falsenegative (e.g., missed braking event), the driver is responsible fordetecting the object-in-path and braking. Similarly, in conventional AEBsystems where a single path is implemented, either the confidencethreshold for activating AEB may be so high that false negative eventsmay occur leading to collisions or too low, resulting in false positivedetections that cause unnecessary braking events, which could lead topassenger discomfort or even a rear collision with a trailing vehicle.

Furthermore, conventional AEB systems limit their field of view orsensory field to portions of the environment in front of the vehicle.With this limited information, AEB systems do not take into account anytrailing vehicles or objects. As a result, conventional AEB systemsgenerally only include a single braking torque profile—which is to brakeas fast as possible as late as possible—when a determination to brake ismade. This can result in a rear collision with an undetected trailingvehicle even where the forward collision is avoided.

Furthermore, traditional AEB systems apply the brakes of a vehicle inthe same manner regardless of the vehicle's environment. For example,the AEB system will apply the brakes the same way regardless of whetherthere is ample space in front of vehicle that would allow for more timeto brake. As another example, conventional AEB systems disregardactivity in the rear of vehicle when deciding the level at which toengage the brakes. By uniformly applying a vehicle's brakes with noconsideration of a vehicle's environment, current AEB systems may notbrake efficiently and may apply full force causing unnecessary jerkingof the vehicle's passengers.

SUMMARY

Embodiments of the present disclosure relate to leveraging rear-viewsensors for automatic emergency braking in autonomous machineapplications. Systems and methods are disclosed that receive and analyzesensor data of a vehicle representative of a front-view, side-view,and/or rear-view of the vehicle. By analyzing the sensor data frommultiple perspectives of a vehicle, an automatic emergency braking (AEB)system and/or a collision mitigation warning (CMW) system of the vehiclemay make activation determinations that are more in tune with thesurrounding environment of the vehicle. For example, trigger oractivation thresholds for single or multi-path AEB systems may beadjusted based on the presence of other actors to the front, side,and/or rear of the vehicle—e.g., where another actor is trailing thevehicle, the activation threshold may be increased to avoid a collisionwith the trailing actor. Additionally, based on the analyzed sensor dataand activation criteria being met, embodiments of the present disclosuremay determine the proper amount of force—e.g., corresponding to abraking profile—to apply when the AEB system is activated. In thismanner, embodiments of the present disclosure leverage a more holisticunderstanding of the surrounding environment of a vehicle—including theenvironment to a rear and/or a side of the vehicle—to make dynamicadjustments to activation criteria and/or braking profiles correspondingto an AEB system of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for leveraging rear-view sensors forautomatic emergency braking (AEB) in autonomous machine applications aredescribed in detail below with reference to the attached drawingfigures, wherein:

FIG. 1 is a block diagram of an example system architecture forleveraging rear-view sensors for AEB in autonomous machine applications,in accordance with some embodiments of the present disclosure;

FIGS. 2A-2D depict example scenarios for leveraging rear-view sensorsfor AEB in autonomous machine applications, in accordance with someembodiments of the present disclosure;

FIG. 3 is a flow diagram showing a method for leveraging rear-viewsensors for AEB in autonomous machine applications, in accordance withsome embodiments of the present disclosure;

FIG. 4A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 4B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 4A, in accordance with someembodiments of the present disclosure;

FIG. 4C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 4A, in accordance with someembodiments of the present disclosure;

FIG. 4D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 4A, in accordancewith some embodiments of the present disclosure; and

FIG. 5 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to leveraging rear-viewsensors for automatic emergency braking (AEB) in autonomous machineapplications. Although the present disclosure may be described withrespect to an example autonomous vehicle 400 (alternatively referred toherein as “vehicle 400”, “ego-vehicle 400”, or “autonomous vehicle 400,”an example of which is described with respect to FIGS. 4A-4D, this isnot intended to be limiting. For example, the systems and methodsdescribed herein may be used by, without limitation, non-autonomousvehicles, semi-autonomous vehicles (e.g., in one or more adaptive driverassistance systems (ADAS)), robots, warehouse vehicles, off-roadvehicles, flying vessels, boats, shuttles, emergency response vehicles,motorcycles, electric or motorized bicycles, aircraft, constructionvehicles, underwater craft, drones, and/or other vehicle types. Inaddition, although the present disclosure may be described with respectto autonomous driving or ADAS systems—and specifically with respect toAEB systems and collision mitigation warning (CMW) systems—this is notintended to be limiting. For example, the systems and methods describedherein may be used in a simulation environment (e.g., to test accuracyof an AEB system and/or a CMW system during simulation), in robotics,aerial systems, boating systems, and/or other technology areas, such asfor control operations, obstacle and collision avoidance, and/or otherprocesses.

Systems and methods disclosed herein relate to adjusting the AEB and/orCMW triggering point or level and/or adjusting a braking profile (e.g.,an amount of torque over time) while accounting for objects to the rearof the vehicle. In contrast to conventional systems, such as thosedescribed herein, the system of the present disclosure may leverageinformation with regards to objects to the front, side, and rear of thevehicle to make AEB activation and braking profile decisions. Forexample, using LIDAR sensors, RADAR sensors, ultrasonic sensors,cameras, and/or other sensor types, embodiments of the presentdisclosure are able to detect objects to a rear of the vehicle todetermine an AEB activation trigger or level. In such an example, whenan object is not detected to the rear of the vehicle, the AEB activationtrigger may be reduced relative to when an object is present, or when anobject is closely trailing. Where no trailing vehicle or other dynamicobject is detected, for example, the AEB activation trigger selected mayonly require that a single path or determination to indicate that AEBshould be activated. Where a vehicle is trailing, but is beyond athreshold distance or is also braking, the AEB activation triggerselected may require both determination paths be in agreement, but thebraking profile may be adjusted to allow for a more aggressive brakingprofile—e.g., because the likelihood of collision with the trailingvehicle is reduced. As another example, where a vehicle is closelytrailing, the AEB activation trigger may be the strictest, and mayrequire that all sources of activation determinations are in agreement.In addition, in such an example, the distance and/or actions of theobject to the front of the vehicle that caused the AEB activationdeterminations may be taken into account—e.g., if the object is notstopped or braking, or is beyond a threshold distance, the brakingprofile selected may be less aggressive, allowing the vehicle to come toa stop over a longer period of time to aid in avoiding a collision withthe trailing vehicle.

As described herein, the braking profile for the AEB activation may beadjusted based on information about the presence and location of objectsto the front, rear, and/or side of the vehicle. For example, where anobject is not trailing, the braking profile may be more aggressive,where an object is trailing at a distance, the braking profile may beaggressive but less aggressive than when no object is present, and wherean object is closely trailing, the braking profile may be lessaggressive so long as a collision with the object forward of the vehiclewill be avoided or at least mitigated. In addition, past trajectoriesand/or predicted trajectories of objects in the environment may beleveraged for braking profile determinations. For example, where anobject is in an adjacent lane and speeds up to pass and change lanesclosely in front of the vehicle causing the AEB system to activate, thistrajectory information may be tracked in order to allow for a lessaggressive braking profile where a trailing vehicle is present—e.g.,because the passing object is likely to continue to gain distance fromthe vehicle. As such, by accounting for objects around the vehicle—andnot only to the front of the vehicle—the AEB system may engage withvarying braking profiles to increase the likelihood of collisionavoidance, to reduce the likelihood of mechanical issues from excesstorque on the vehicle, and to make the experience of passengers moreenjoyable by not executing braking with unnecessary amounts of force.

As one example showcasing the benefits of the present disclosure,consider a scenario where a trailing object is following a vehicle veryclosely. If the vehicle analyzes and determines that the AEB systemneeds to be engaged due to a potential collision to the front of thevehicle (as a result of another object), the AEB system of the presentdisclosure may also leverage information about the trailing object—e.g.,the distance of the object from the vehicle, the speed of the object,whether the object is braking, etc.—to determine whether the vehicleshould adjust the AEB activation level and/or braking profile to avoidor reduce the severity of a collision with the trailing object. In somescenarios, the braking profile may be adjusted even if it means someimpact with an object to the front of the vehicle, if this determinationis likely to reduce the collective severity of the collision(s).

As such, embodiments of the present disclosure leverage the availabilityof additional information with regards to the surrounding environment toadjust AEB activation triggers or levels and/or braking profiles forwhen AEB activation occurs. In contrast to conventional systems, theadjustment of the AEB activation triggers may allow the vehicle toaccount for false negatives in a way that does not affect other objectsin the environment—e.g., because the AEB activation trigger may still bestricter when a trailing vehicle is present. In addition, by adjustingthe braking profiles, the potential for collision with objects to thefront, rear, and side of the vehicle may be reduced, and the overallsafety and security of the passengers of the vehicle as well as thesurrounding objects may be increased.

Now with reference to FIG. 1, FIG. 1 is an example system 100 suitablefor leveraging rear-view sensors for AEB in autonomous machineapplications, in accordance with some embodiments of the presentdisclosure. It should be understood that this and other arrangementsdescribed herein are set forth only as examples. Other arrangements andelements (e.g., machines, interfaces, functions, orders, groupings offunctions, etc.) may be used in addition to or instead of those shown,and some elements may be omitted altogether. Further, many of theelements described herein are functional entities that may beimplemented as discrete or distributed components or in conjunction withother components, and in any suitable combination and location. Variousfunctions described herein as being performed by entities may be carriedout by hardware, firmware, and/or software. For instance, variousfunctions may be carried out by a processor executing instructionsstored in memory.

At a high level, the system 100 may execute a process for AEBdeterminations—e.g., as indicated by the flow of the arrows FIG. 1.However, the illustration of FIG. 1 is not intended to be limiting, andthe process may be executed in a different order and/or may includeadditional or alternative components, features, and/or data than theillustration of FIG. 1. The system 100 may process sensor data 102through a vehicle environment analyzer 108, an activation determiner114, a profile determiner 116, an AEB control component 118, a vehiclecontrol component 120, and/or one or more additional or alternativecomponents, features, modules, and/or functionalities. In somenon-limiting embodiments, depending on the particular implementation,some components may contain one or more sub-components. For example, thevehicle environment analyzer 108 may be comprised of and/or include aforward vehicle analyzer 110 and rear vehicle analyzer 112. In addition,although the sensor data 102 is illustrated as including forward sensordata 104 and rearward sensor data 106, this is not intended to belimiting. The sensor data 102 may include sensor data from sensors withfields of view and/or sensory fields that capture any portion of theenvironment around the vehicle 400.

In some embodiments, the sensor data 102 may include any type of sensordata, such as but not limited to image data from one or more cameras,LIDAR data from one or more LIDAR sensors 464, RADAR data from one ormore RADAR sensors 460, audio data from one or more microphones 496,sensor data 102 from one or more other sensors of the vehicle 400,and/or sensor data 102 from one or more sensors of another object type(e.g., a robot, a watercraft, etc.). In some examples, the activationdeterminer 114 and/or the profile determiner 116 may use output from thevehicle environment analyzer 108 to determine activation criteria and/ora brake profile setting, respectively, based on the sensor data 102. Theoutputs from the activation determiner 114 and/or the profile determiner116 may be used by control component(s) of the vehicle 400 (e.g.,controller(s) 436, ADAS system 438, SOC(s) 404, software stack 422,and/or other components of the autonomous vehicle 400) to aid thevehicle 400 in performing one or more operations (e.g., activationcriteria determination, braking profile determination, etc.) within anenvironment.

In embodiments, the sensor data 102 may be comprised of the forwardsensor data 104, the rearward sensor data 106, and/or other sensor datafrom one or more additional or alternative fields of view or sensoryfields of the vehicle 400. In a non-limiting embodiment, forward sensordata 104 may generally include any type of sensor data with a field ofview to a front of the vehicle 400 (e.g., image data captured by one ormore cameras of the autonomous vehicle 400 in the front portion of thevehicle 400 and/or with a field of view thereof, sensor data fromforward-facing LIDAR sensors and/or RADAR sensors, etc.). Similarly, ina non-limiting embodiment, rearward sensor data 104 may generallyinclude any type of sensor data with a field of view to a rear of thevehicle 400 (e.g., image data captured by one or more cameras of theautonomous vehicle 400 in the rear portion of the vehicle 400 and/orwith a field of view thereof, sensor data from rearward-facing LIDARsensors and/or RADAR sensors, etc.). As such, the sensor data 102 mayinclude image data and other sensor data types that represent fields ofview and/or sensory fields from multiple perspectives of the vehicle400. Thus, any number of sensors may be deployed within the system 100and leveraged by the vehicle environment analyzer 108, the activationdeterminer 114, the profile determiner 116, and/or other components ofthe system 100. As a result, the sensor data 102 captured from multipleperspectives of the vehicle 400 may allow for enhanced perception in AEBand/or CMW system—thereby resulting in a safer, more comfortable ridefor the passengers of the vehicle 400.

The vehicle environment analyzer 108 may use as input one or more imagesand/or other sensor data representations (e.g., LIDAR data, RADAR data,etc.) as represented by sensor data 102. The vehicle environmentanalyzer 108 may analyze the sensor data 102 to determine whether anobject is detected in at least a portion of the field of view and/orsensory field to a front, side, and/or rear of the vehicle 400. In somenon-limiting embodiments, the vehicle analyzer 108 may include a forwardvehicle analyzer 110 (e.g., to process information corresponding to arear of the vehicle 400) and a rear vehicle analyzer 112 (e.g., toprocess information corresponding to a front of the vehicle 400).However, this is not intended to be limiting, and the system 100 mayinclude additional or alternative analyzers, such as a side vehicleanalyzer, an upward vehicle analyzer, and/or the like.

In any example, the vehicle environment analyzer 108 may analyze orotherwise process the sensor data 102 to determine if a vehicle or otherdynamic actor is present to a front, rear, and/or side of the vehicle400. In addition, information corresponding to any detected vehicles ordynamic actors may be determined by the vehicle environment analyzer108—such as a distance of the actor from the vehicle 400, a speed,velocity, acceleration, or deceleration of the actor, whether or not theactor is braking, changing lanes, providing another signal, or makinganother maneuver, and/or the like. In some embodiments, the vehicleenvironment analyzer 108 may track other vehicles or actors over time todetermine past trajectories and/or estimate future trajectories, and thesystem 100 may use this information to further understand thesurrounding environment for making activation trigger determinationsand/or setting braking profiles.

Once an understanding of the surrounding vehicle 400 is determined usingthe vehicle environment analyzer 108, the activation determiner 114and/or the profile determiner 116 may perform additional operationsbased on the vehicle surroundings. For example, at a high level, theactivation determiner 114 may determine—dynamically, inembodiments—activation criteria for activating or triggering an AEBand/or CMW system based on the existence of, locations of, and/orinformation related to other vehicles or dynamic actors in theenvironment. The activation criteria may include, without limitation,one or more criteria that are to be met to activate the AEB and/or CMWand/or that—when not met—do not cause activation of these systems. Assuch, the activation determiner 114 may analyze the information of thesurrounding environment of the vehicle 400 and adjust, change, orotherwise set the activation criteria at any point in time. For example,where a single path or input criteria is used or available to the AEBand/or CMW system, the activation determiner 114 may set athreshold—such as a confidence threshold—that defines the activationcriteria at that period of time. As another example, where two or morepaths or input criteria area used or available to the AEB and/or CMWsystem, the activation determiner 114 may adjust thresholds for one ormore of the paths, may require that only one of the paths be satisfied(e.g., provide an indication that AEB and/or CMW should be triggered),and/or may require that two or more of the paths be satisfied. As aresult, the activation determiner 114 may leverage the information fromthe vehicle environment analyzer 108 to set an activation trigger orcriteria for the vehicle 400.

As an example, if the activation determiner 114 determines—e.g., basedon the output of the vehicle environment analyzer 108—that a vehicle infront the ego-vehicle 400 is braking or that the ego-vehicle isotherwise closing a gap between the two vehicles, and the activationdeterminer 114 determines that there is no rear trailing vehicle, theactivation determiner 114 may set the activation criteria such that theAEB and/or CMW system may brake, otherwise slow down, and/or provide awarning signal earlier and/or based on a lower degree of certainty thatan object is present to the front of the vehicle. This determination maybe made because the risk of a rear collision is reduced when there areno trailing vehicles determined to be present, so a false positiveinduced braking and/or warning may not increase risk and may actuallypotentially increase safety of the passengers and the surroundingvehicles by not increasing the activation threshold so high as to causea false negative. In a similar situation, but where a trailing vehicleis present, the activation determiner 114 may determine to increase theconfidence or activation standard because a false positive braking mayincrease the likelihood of a collision with the rear-trailing vehicle.In such an example, where two input criteria (e.g., a LIDAR data inputand an image data input) are relied upon for the AEB and/or CMW system,the activation criteria may require that both input criteria provide anindication that the AEB and/or CMW system should be activated prior toactivating the system(s). As another example, where a vehicle is passingthe ego-vehicle 400 and a rear-trailing vehicle is present, thetrajectory of the vehicle may be monitored such that, even where thevehicle cuts closely in front of the ego-vehicle 400, the activationcriteria may be reduced to avoid activating AEB and/or CMW as a resultof the other vehicle cutting in front of the ego-vehicle. Thisdetermination may be made by the activation determiner 114 because thelikelihood of a collision is reduced when a vehicle is changing lanesand cutting in front of the ego-vehicle 400, so the tracking of thevehicle trajectory through the lane change may provide an indication tothe ego-vehicle 400 that the AEB and/or CMW activation criteria may beincreased and may thus not result in a false positive where a trailingvehicle is present. In a similar situation but with a trailing vehiclepresent, the AEB and/or CMW activation criteria may be relaxed such thatactivation is more likely, because without a trailing vehicle presentthe only current risk is the cut-in vehicle, so preemptively applyingthe brakes may provide the greatest net increase in safety. In addition,as described in more detail herein with respect to the profiledeterminer 116, in any of these examples the braking profile of the AEBsystem may be adjusted based on the surrounding environment such thateach AEB activation does not result in an abrupt brakingaction—especially where no rear vehicle is present and/or a forwardvehicle is accelerating, is a greater distance from the ego-vehicle,and/or the like.

The profile determiner 116 may determine—dynamically, in embodiments—abraking profile setting for the vehicle 400 based on information outputby the vehicle environment analyzer 108. For example, the profiledeterminer 116 may determine and/or set a level, sensitivity, trajectory(e.g., in three-dimensional (3D) world-space), path, and/or othercriteria or output corresponding to the braking profile. As such, basedon information of dynamic actors (e.g., presence, location, speed,actions such as braking, changing lanes, etc.) in the surroundingenvironment of the vehicle 400 and/or the activation criteria (e.g.,relaxed, strict, etc.), the profile determiner 116 may set the brakingprofile for the ego-vehicle 400. In some embodiments, the brakingprofile may be set prior to activation of AEB such that, if activated,the braking profile for the AEB will be in accordance with the presetbraking profile. In other embodiments, the braking profile may be set atthe time of and/or after activation of the AEB, such that, onceactivated, the information from the vehicle environment analyzer 108 maybe used to determine the safest and/or most effective braking profilefor the ego-vehicle 400.

In a non-limiting embodiment, the profile determiner 166 may determinethe braking profile setting for the vehicle 400 based on characteristicsof the vehicle 400. For example, if the vehicle 400 is a semi-trailertruck, the profile determiner 116 may adjust the braking profile settingto take into account the weight of the semi-trailer truck and applybrake pressure in way fits to a semi-trailer truck. In anothernon-limiting embodiment, the profile determiner 116 may also determinethe braking profile setting for a vehicle based on environmentalcharacteristics. For example, based on the weather conditions orconditions of the roadway (e.g., wet, snow, dirt, gravel, potholes(e.g., as determined using an HD map, one or more deep neural networks,etc.), speed bumps, uneven pavement, etc.), the braking profile settingof a vehicle may be adjusted to take into account the weather and/orother road conditions. As such, where the driving surface is icy or wet,the profile determiner 116 may adjust the braking profile to brake lessaggressively and also brake in a gradual manner to avoid slipping orsliding. Additionally, in non-limiting embodiments, the activationcriteria be further based on the past trajectory of detected objects.For example, as described in an example above, where a vehicle ispassing the ego-vehicle 400 and then cuts in front of the ego-vehicle,this past trajectory information may be leveraged to determine that thebraking profile should be less aggressive due to the likelihood that thecut-in vehicle is going to continue to accelerate and create distancefrom the ego-vehicle. Thus, the profile determiner 116 may determine thebraking profile setting based on any or all available sensor data 102,determinations by the vehicle environment analyzer 108, HD map data,determinations by the activation determiner, and/or information from oneor more other systems of the vehicle 400 that may deploy one or moredeep neural networks (e.g., drivable free-space information, roadprofile information, lane location and type information, wait conditioninformation, predicted future trajectory information of surroundingactors, etc.).

As another example, consider when there is a vehicle a short distance tothe front of the ego-vehicle 400, but there is no rear trailing vehicle.In this case, the profile determiner 116 may determine that the brakingprofile can be aggressive or adjusted to brake the vehicle more quicklybecause there is less of a risk of a rear collision—thereby allowing theego-vehicle 400 to brake hard to ensure that a forward collision isavoided. In a similar scenario where a trailing vehicle is present, thebraking profile may be less aggressive—e.g., the least aggressive tostill avoid a forward collision—to avoid or reduce the impact of a rearcollision with the rear-trailing vehicle. In some scenarios, such aswhere a front and rear vehicle are within close proximity to theego-vehicle, the braking profile may be set by the profile determiner116 to reduce the overall intensity or damage from a collision with thefront and/or rear trailing vehicle. As such, where a collision seemsinevitable, the braking profile may be determined to cause the lowestnet damage and/or the greatest net safety.

Referring now to FIGS. 2A-2D, FIGS. 2A-2D depict example scenarios forleveraging rear-view and/or other surrounding sensors for AEB inautonomous machine applications, in accordance with some embodiments ofthe present disclosure. For the purposes of discussion, it may beassumed that some, none, or all of the cars illustrated in FIGS. 2A-2Dinclude AEB and/or CMW systems—such as but not limited to thosedescribed herein with respect to the system 100. As such, activationdeterminations and/or braking profiles may be discussed with respect toeach different car within a single illustration, or may be discussedwith one or more cars within a single illustration. In examples wheretwo or more cars or other dynamic actors have AEB and/or CMW systemssimilar to those described herein, the cumulative benefit may serve tofurther increase the safety and reliability of the AEB and/or CMWsystems.

With reference to FIG. 2A, FIG. 2A shows three cars 202A, 204A, and 206Ain the middle lane of a three-lane highway (although it is contemplatedthat the location of the cars in FIG. 2A are for example purposes onlyand cars 202A, 204A, and 206A may be located in any suitable environmentand/or orientation or pose therein). As shown, the car 204A is within ashort distance in front of the car 202A and the car 206A is within ashort distance behind the car 202A. Employing embodiments of the system100 discussed in conjunction with at least FIG. 1, the car 202A maydetermine that the car 206A is following too closely based on the shortdistance between the car 202A and the car 204A. As such, the activationcriteria and braking profile setting of the AEB system in the car 202Amay be adjusted based on the sensor data analyzed and representative ofthe car 206A and the car 204A. As such, as indicated by braking lines onthe road surface, the car 202A would likely have to brake quickly over ashort distance to avoid a collision with one or both of the cars 206Aand 204A (e.g., a potential collision being denoted by an exclamationpoint “!”). For example, if the car 206A is not braking when the car202A engages its AEB system (e.g., because activation criteria has notbeen met), the braking profile setting of the car 202A may be adjustedto lessen the collision with the car 206A, even if it means minimalimpact with the car 204A. To determine whether the car 206A is braking,the sensor data 102 may be analyzed—over time—to determine that the car206A is decelerating and/or beginning to increase its distance from thecar 202A. By analyzing the rearward sensor data of the car 202A,embodiments of the present disclosure can measure the distance over timeof the car 206A and determine its velocity as well as determine thetrajectory of the car 206A. In this way, embodiments of the presentdisclosure may be employed in the car 202A to determine activationcriteria and determine the braking profile setting based on analyzeddata regarding the car 204A and the car 206A.

In some instances, the car 202A may not adjust the activation criteriaor braking profile setting based on analyzed sensor data regarding thecar 204A and the car 206A. For example, where it is determined that thecar 204A and the car 206A are maintaining a consistent speed and theirtrajectory is not expected to change, the activation criteria andbraking profile setting of the AEB system in the car 202A may not beadjusted. However, in other situations, where one or both of the car204A and the car 206A changes its speed and/or trajectory, theactivation criteria and braking profile setting of the AEB system in thecar 202A may be adjusted accordingly.

Turning now to FIG. 2B, FIG. 2B shows three cars 202B, 204B, and 206B inthe middle lane of a three-lane highway. As shown, the car 204B iswithin a short distance in front of the car 202B and the car 206B iswithin a further distance behind the car 202B. Employing embodiments ofthe system 100 discussed in conjunction with at least FIG. 1, the car202B may adjust the activation criteria and/or braking profile settingof its AEB system to be more aggressive given the amount of space fromcar 206B. In this way, the car 202B may adjust the activation criteriato be more sensitive (e.g., more strict, requiring a higher confidencethat the car 204B or other actor is present and/or within a thresholddistance to the front of the car 202B) and brake the car 202B morequickly if a possible collision is detected. This may be because the car206B is outside of a threshold distance from the car 202B, therebyreducing the likelihood of a collision with the trailing car 206B whereaggressive braking is employed. As a result, the car 202B may avoid acollision with the car 204B while also ensuring that the likelihood of acollision with the car 206B is reduced, thus making the AEB systemsafer. As such, the braking distance indicated by the brake lines on thedriving surface may be similar length to that of FIG. 2A, but thelikelihood of a collision with respect to the car 206B may be reduced(e.g., the car 206B does not include a “!” indicating a possiblecollision).

Referring now to FIG. 2C, FIG. 2C shows three cars 202C, 204C, and 206Cin the middle lane of a three-lane highway. As shown, the car 206C iswithin a short distance to the rear of the car 202C, and the car 204C isa further distance in front of the car 202C. Employing embodiments ofthe system 100 discussed in conjunction with at least FIG. 1, the car202C may adjust the activation criteria and/or braking profile settingof its AEB system to be less aggressive given the amount of space fromthe car 204C. As such, the car 202C may adjust the activation criteriato be less sensitive and brake the vehicle less quickly (as indicated bythe length of the brake lines on the driving surface as compared toFIGS. 2A, 2B, and 2D) if a possible collision in front of the car 202Cis detected because the car 206C is trailing closely behind the rear ofthe car 202C. In this way, the car 202C may avoid unnecessarily brakingor braking too hard and causing an unintended accident with the closelytrailing car 206C. In some instances, the car 202C may further adjustthe activation criteria and/or braking profile setting of its AEB systemif it is determined that the car 204C is slowing down and decreasing itsdistance from car the 202C. In this case, the activation criteria and/orbraking profile settings may be readjusted based the information—such asto brake more aggressively but still not aggressively enough to cause arear collision with the car 206C.

With reference to FIG. 2D, FIG. 2D shows two cars 202D and 204D in themiddle lane of a three-lane highway. As shown, the car 204D is within ashort distance in front of the car 202D. Employing embodiments of thesystem 100 discussed in conjunction with at least FIG. 1, the car 202Dmay adjust the activation criteria and/or braking profile setting of itsAEB system to be more aggressive—e.g., because there are no vehicles orother dynamic objects behind the car 202D. As such, the car 202D mayadjust the activation criteria to be more sensitive and brake thevehicle more quickly if a possible collision is detected with the car204D because the potential for a rear collision is minimized. In thisway, the car 202D may avoid a collision (indicated by the “!”) with thecar 204D while also ensuring that the likelihood of a collision with anyobjects behind car 202D is reduced. The short braking distance isindicated by the length of the brake lines on the driving surface.

With reference again to FIG. 1, once the activation criteria and/or thebraking profile are determined, the AEB control component 118 may usethis information as input to determine when and how to activate the AEBsystem of a vehicle. For example, the AEB control component 118 maydetermine that the activation criteria has been met and generate outputto the vehicle control component 120 that causes the vehicle controlcomponent to engage the AEB system of the vehicle 400—e.g., via one ormore actuation components. Once activated, the AEB system may cause thebraking profile to be activated during the execution of the AEBactivation. In non-limiting embodiments, the AEB control component 118may determine whether a threshold level of agreement is met among two ormore activation criteria as set by the activation determiner 114 and/orwhether a threshold confidence or prediction of an individual activationcriteria(s) is met. In this way, different activation criteria may havedifferent agreement levels that must meet a threshold agreement level inorder for AEB activation to occur. As such, the vehicle controlcomponent 120 may engage the vehicle's AEB system when the activationcriteria determined from the activation determiner 114 are met and mayapply the braking profile setting determined by the profile determiner116. Thus, embodiments of the present disclosure may take into account amore holistic view of the surrounding environment of the vehicle 400when determining when and how to engage the AEB system of the vehicle400.

Now referring to FIG. 3, each block of method 300, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 300 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 300 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,the method 300 is described, by way of example, with respect to thesystem of FIG. 1. However, this method 300 may additionally oralternatively be executed by any one system, or any combination ofsystems, including, but not limited to, those described herein.

FIG. 3 is a flow diagram showing a method 300 leveraging rear-viewsensors for automatic emergency braking in autonomous machineapplications, in accordance with some embodiments of the presentdisclosure. The method 300, at block 302, includes receiving sensor datagenerated using a sensor of a vehicle, the sensor having a field of viewor a sensory field to a rear of the vehicle. For example, the sensordata 102—such as the rearward sensor data 106—may be received.

The method 300, at block 304, includes analyzing the sensor data todetermine whether an object is detected in at least a portion of thefield of view or the sensory field. For example, the vehicle environmentanalyzer 108 may analyze the sensor data 102 to determine whether avehicle and/or another dynamic object is present in the field of viewand/or the sensor field of a sensor(s) of the vehicle 400.

The method 300, at block 306, includes determining activation criteriafor activating an AEB system. For example, the activation determiner 114may determine activation criteria for activating AEB by the AEB controlcomponent 118.

The method 300, at block 308, includes determining a braking profilesetting for the vehicle once the AEB system is activated. For example,the profile determiner 116 may determine a braking profile—or a settingcorresponding thereto—for the AEB system if the activation criteria ismet.

Example Autonomous Vehicle

FIG. 4A is an illustration of an example autonomous vehicle 400, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 400 (alternatively referred to herein as the “vehicle400”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,and/or another type of vehicle (e.g., that is unmanned and/or thataccommodates one or more passengers). Autonomous vehicles are generallydescribed in terms of automation levels, defined by the National HighwayTraffic Safety Administration (NHTSA), a division of the US Departmentof Transportation, and the Society of Automotive Engineers (SAE)“Taxonomy and Definitions for Terms Related to Driving AutomationSystems for On-Road Motor Vehicles” (Standard No. J3016-201806,published on Jun. 15, 2018, Standard No. J3016-201609, published on Sep.30, 2016, and previous and future versions of this standard). Thevehicle 400 may be capable of functionality in accordance with one ormore of Level 0-Level 5 of the autonomous driving levels. For example,the vehicle 400 may be capable of momentary assistance (Level 0), driverassistance (Level 1), partial automation (Level 2), conditionalautomation (Level 3), high automation (Level 4), and/or full automation(Level 5), depending on the embodiment.

The vehicle 400 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 400 may include a propulsion system450, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 450 may be connected to a drive train of the vehicle400, which may include a transmission, to enable the propulsion of thevehicle 400. The propulsion system 450 may be controlled in response toreceiving signals from the throttle/accelerator 452.

A steering system 454, which may include a steering wheel, may be usedto steer the vehicle 400 (e.g., along a desired path or route) when thepropulsion system 450 is operating (e.g., when the vehicle is inmotion). The steering system 454 may receive signals from a steeringactuator 456. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 446 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 448 and/or brakesensors.

Controller(s) 436, which may include one or more system on chips (SoCs)404 (FIG. 4C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle400. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 448, to operate thesteering system 454 via one or more steering actuators 456, to operatethe propulsion system 450 via one or more throttle/accelerators 452. Thecontroller(s) 436 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 400. The controller(s) 436 may include a first controller 436for autonomous driving functions, a second controller 436 for functionalsafety functions, a third controller 436 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 436 forinfotainment functionality, a fifth controller 436 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 436 may handle two or more of the abovefunctionalities, two or more controllers 436 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 436 may provide the signals for controlling one ormore components and/or systems of the vehicle 400 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 458 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 460, ultrasonic sensor(s) 462, LIDARsensor(s) 464, inertial measurement unit (IMU) sensor(s) 466 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 496, stereo camera(s) 468, wide-view camera(s) 470(e.g., fisheye cameras), infrared camera(s) 472, surround camera(s) 474(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 498,speed sensor(s) 444 (e.g., for measuring the speed of the vehicle 400),vibration sensor(s) 442, steering sensor(s) 440, brake sensor(s) (e.g.,as part of the brake sensor system 446), and/or other sensor types.

One or more of the controller(s) 436 may receive inputs (e.g.,represented by input data) from an instrument cluster 432 of the vehicle400 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 434, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle400. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 422 of FIG. 4C), location data(e.g., the vehicle's 400 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 436,etc. For example, the HMI display 434 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 400 further includes a network interface 424 which may useone or more wireless antenna(s) 426 and/or modem(s) to communicate overone or more networks. For example, the network interface 424 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 426 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 4B is an example of camera locations and fields of view for theexample autonomous vehicle 400 of FIG. 4A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle400.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 400. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 420 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 400 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 436 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (“LDW”), Autonomous Cruise Control(“ACC”), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 470 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.4B, there may any number of wide-view cameras 470 on the vehicle 400. Inaddition, long-range camera(s) 498 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 498 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 468 may also be included in a front-facingconfiguration. The stereo camera(s) 468 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 468 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 468 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 400 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 474 (e.g., four surround cameras 474 asillustrated in FIG. 4B) may be positioned to on the vehicle 400. Thesurround camera(s) 474 may include wide-view camera(s) 470, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 474 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 400 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 498,stereo camera(s) 468), infrared camera(s) 472, etc.), as describedherein.

FIG. 4C is a block diagram of an example system architecture for theexample autonomous vehicle 400 of FIG. 4A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 400 in FIG.4C are illustrated as being connected via bus 402. The bus 402 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 400 used to aid in control of various features and functionalityof the vehicle 400, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 402 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 402, this is notintended to be limiting. For example, there may be any number of busses402, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses402 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 402 may be used for collisionavoidance functionality and a second bus 402 may be used for actuationcontrol. In any example, each bus 402 may communicate with any of thecomponents of the vehicle 400, and two or more busses 402 maycommunicate with the same components. In some examples, each SoC 404,each controller 436, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle400), and may be connected to a common bus, such the CAN bus.

The vehicle 400 may include one or more controller(s) 436, such as thosedescribed herein with respect to FIG. 4A. The controller(s) 436 may beused for a variety of functions. The controller(s) 436 may be coupled toany of the various other components and systems of the vehicle 400, andmay be used for control of the vehicle 400, artificial intelligence ofthe vehicle 400, infotainment for the vehicle 400, and/or the like.

The vehicle 400 may include a system(s) on a chip (SoC) 404. The SoC 404may include CPU(s) 406, GPU(s) 408, processor(s) 410, cache(s) 412,accelerator(s) 414, data store(s) 416, and/or other components andfeatures not illustrated. The SoC(s) 404 may be used to control thevehicle 400 in a variety of platforms and systems. For example, theSoC(s) 404 may be combined in a system (e.g., the system of the vehicle400) with an HD map 422 which may obtain map refreshes and/or updatesvia a network interface 424 from one or more servers (e.g., server(s)478 of FIG. 4D).

The CPU(s) 406 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 406 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 406may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 406 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 406 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)406 to be active at any given time.

The CPU(s) 406 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 406may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 408 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 408 may be programmable and may beefficient for parallel workloads. The GPU(s) 408, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 408 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 408 may include at least eight streamingmicroprocessors. The GPU(s) 408 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 408 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 408 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 408 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 408 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 408 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 408 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 408 to access the CPU(s) 406 page tables directly. Insuch examples, when the GPU(s) 408 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 406. In response, the CPU(s) 406 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 408. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 406 and the GPU(s) 408, thereby simplifying the GPU(s) 408programming and porting of applications to the GPU(s) 408.

In addition, the GPU(s) 408 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 408 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 404 may include any number of cache(s) 412, including thosedescribed herein. For example, the cache(s) 412 may include an L3 cachethat is available to both the CPU(s) 406 and the GPU(s) 408 (e.g., thatis connected both the CPU(s) 406 and the GPU(s) 408). The cache(s) 412may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 404 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 400—such as processingDNNs. In addition, the SoC(s) 404 may include a floating point unit(s)(FPU(s))—or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 406 and/or GPU(s) 408.

The SoC(s) 404 may include one or more accelerators 414 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 404 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 408 and to off-load some of the tasks of theGPU(s) 408 (e.g., to free up more cycles of the GPU(s) 408 forperforming other tasks). As an example, the accelerator(s) 414 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 414 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 408, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 408 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 408 and/or other accelerator(s) 414.

The accelerator(s) 414 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 406. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 414 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 414. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 404 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 414 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 0-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 466 output thatcorrelates with the vehicle 400 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 464 or RADAR sensor(s) 460), amongothers.

The SoC(s) 404 may include data store(s) 416 (e.g., memory). The datastore(s) 416 may be on-chip memory of the SoC(s) 404, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 416 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 412 may comprise L2 or L3 cache(s) 412. Reference to thedata store(s) 416 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 414, as described herein.

The SoC(s) 404 may include one or more processor(s) 410 (e.g., embeddedprocessors). The processor(s) 410 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 404 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 404 thermals and temperature sensors, and/ormanagement of the SoC(s) 404 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 404 may use thering-oscillators to detect temperatures of the CPU(s) 406, GPU(s) 408,and/or accelerator(s) 414. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 404 into a lower powerstate and/or put the vehicle 400 into a chauffeur to safe stop mode(e.g., bring the vehicle 400 to a safe stop).

The processor(s) 410 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 410 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 410 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 410 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 410 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 410 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)470, surround camera(s) 474, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 408 is not required tocontinuously render new surfaces. Even when the GPU(s) 408 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 408 to improve performance and responsiveness.

The SoC(s) 404 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 404 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 404 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 404 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 464, RADAR sensor(s) 460,etc. that may be connected over Ethernet), data from bus 402 (e.g.,speed of vehicle 400, steering wheel position, etc.), data from GNSSsensor(s) 458 (e.g., connected over Ethernet or CAN bus). The SoC(s) 404may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 406 from routine data management tasks.

The SoC(s) 404 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 0-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 404 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 414, when combined with the CPU(s) 406, the GPU(s) 408,and the data store(s) 416, may provide for a fast, efficient platformfor level 0-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 0-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 0-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 420) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 408.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 400. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 404 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 496 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 404 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)458. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 462, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 418 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 404 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 418 may include an X86 processor,for example. The CPU(s) 418 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 404, and/or monitoring the statusand health of the controller(s) 436 and/or infotainment SoC 430, forexample.

The vehicle 400 may include a GPU(s) 420 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 404 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 420 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 400.

The vehicle 400 may further include the network interface 424 which mayinclude one or more wireless antennas 426 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 424 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 478 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 400information about vehicles in proximity to the vehicle 400 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 400).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 400.

The network interface 424 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 436 tocommunicate over wireless networks. The network interface 424 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 400 may further include data store(s) 428 which may includeoff-chip (e.g., off the SoC(s) 404) storage. The data store(s) 428 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 400 may further include GNSS sensor(s) 458. The GNSSsensor(s) 458 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. Any number of GNSS sensor(s) 458 may be used, including, forexample and without limitation, a GPS using a USB connector with anEthernet to Serial (RS-232) bridge.

The vehicle 400 may further include RADAR sensor(s) 460. The RADARsensor(s) 460 may be used by the vehicle 400 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 460 may usethe CAN and/or the bus 402 (e.g., to transmit data generated by theRADAR sensor(s) 460) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 460 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 460 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 460may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 400 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 400 lane.

Mid-range RADAR systems may include, as an example, a range of up to 460m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 450 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 400 may further include ultrasonic sensor(s) 462. Theultrasonic sensor(s) 462, which may be positioned at the front, back,and/or the sides of the vehicle 400, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 462 may be used, and different ultrasonic sensor(s) 462 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 462 may operate at functional safety levels of ASILB.

The vehicle 400 may include LIDAR sensor(s) 464. The LIDAR sensor(s) 464may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 464 maybe functional safety level ASIL B. In some examples, the vehicle 400 mayinclude multiple LIDAR sensors 464 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 464 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 464 may have an advertised rangeof approximately 400 m, with an accuracy of 2 cm-3 cm, and with supportfor a 400 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 464 may be used. In such examples,the LIDAR sensor(s) 464 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 400.The LIDAR sensor(s) 464, in such examples, may provide up to a420-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)464 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 400. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)464 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 466. The IMU sensor(s) 466may be located at a center of the rear axle of the vehicle 400, in someexamples. The IMU sensor(s) 466 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 466 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 466 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 466 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 466 may enable the vehicle 400to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 466. In some examples, the IMU sensor(s) 466 and theGNSS sensor(s) 458 may be combined in a single integrated unit.

The vehicle may include microphone(s) 496 placed in and/or around thevehicle 400. The microphone(s) 496 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 468, wide-view camera(s) 470, infrared camera(s) 472,surround camera(s) 474, long-range and/or mid-range camera(s) 498,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 400. The types of cameras useddepends on the embodiments and requirements for the vehicle 400, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 400. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 4A and FIG. 4B.

The vehicle 400 may further include vibration sensor(s) 442. Thevibration sensor(s) 442 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 442 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 400 may include an ADAS system 438. The ADAS system 438 mayinclude a SoC, in some examples. The ADAS system 438 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW) (alternativelyreferred to as collision mitigation warning (CMW)), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 460, LIDAR sensor(s) 464, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 400 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 400 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 424 and/or the wireless antenna(s) 426 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 400), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 400, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 460, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 460, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle400 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 400 if the vehicle 400 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)460, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 400 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 460, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 400, the vehicle 400itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 436 or a second controller 436). For example, in someembodiments, the ADAS system 438 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 438may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 404.

In other examples, ADAS system 438 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 438 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 438indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 400 may further include the infotainment SoC 430 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 430 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 400. For example, the infotainment SoC 430 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 434, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 430 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 438,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 430 may include GPU functionality. The infotainmentSoC 430 may communicate over the bus 402 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 400. Insome examples, the infotainment SoC 430 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 436(e.g., the primary and/or backup computers of the vehicle 400) fail. Insuch an example, the infotainment SoC 430 may put the vehicle 400 into achauffeur to safe stop mode, as described herein.

The vehicle 400 may further include an instrument cluster 432 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 432 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 432 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 430 and theinstrument cluster 432. In other words, the instrument cluster 432 maybe included as part of the infotainment SoC 430, or vice versa.

FIG. 4D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 400 of FIG. 4A, inaccordance with some embodiments of the present disclosure. The system476 may include server(s) 478, network(s) 490, and vehicles, includingthe vehicle 400. The server(s) 478 may include a plurality of GPUs484(A)-484(H) (collectively referred to herein as GPUs 484), PCIeswitches 482(A)-482(H) (collectively referred to herein as PCIe switches482), and/or CPUs 480(A)-480(B) (collectively referred to herein as CPUs480). The GPUs 484, the CPUs 480, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 488 developed by NVIDIA and/orPCIe connections 486. In some examples, the GPUs 484 are connected viaNVLink and/or NVSwitch SoC and the GPUs 484 and the PCIe switches 482are connected via PCIe interconnects. Although eight GPUs 484, two CPUs480, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 478 mayinclude any number of GPUs 484, CPUs 480, and/or PCIe switches. Forexample, the server(s) 478 may each include eight, sixteen, thirty-two,and/or more GPUs 484.

The server(s) 478 may receive, over the network(s) 490 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 478 may transmit, over the network(s) 490 and to the vehicles,neural networks 492, updated neural networks 492, and/or map information494, including information regarding traffic and road conditions. Theupdates to the map information 494 may include updates for the HD map422, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 492, the updated neural networks 492, and/or the mapinformation 494 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 478 and/or other servers).

The server(s) 478 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Once the machinelearning models are trained, the machine learning models may be used bythe vehicles (e.g., transmitted to the vehicles over the network(s) 490,and/or the machine learning models may be used by the server(s) 478 toremotely monitor the vehicles.

In some examples, the server(s) 478 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 478 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 484, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 478 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 478 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 400. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 400, suchas a sequence of images and/or objects that the vehicle 400 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 400 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 400 is malfunctioning, the server(s) 478 may transmit asignal to the vehicle 400 instructing a fail-safe computer of thevehicle 400 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 478 may include the GPU(s) 484 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 5 is a block diagram of an example computing device 500 suitablefor use in implementing some embodiments of the present disclosure.Computing device 500 may include a bus 502 that directly or indirectlycouples the following devices: memory 504, one or more centralprocessing units (CPUs) 506, one or more graphics processing units(GPUs) 508, a communication interface 510, input/output (I/O) ports 512,input/output components 514, a power supply 516, and one or morepresentation components 518 (e.g., display(s)).

Although the various blocks of FIG. 5 are shown as connected via the bus502 with lines, this is not intended to be limiting and is for clarityonly. For example, in some embodiments, a presentation component 518,such as a display device, may be considered an I/O component 514 (e.g.,if the display is a touch screen). As another example, the CPUs 506and/or GPUs 508 may include memory (e.g., the memory 504 may berepresentative of a storage device in addition to the memory of the GPUs508, the CPUs 506, and/or other components). In other words, thecomputing device of FIG. 5 is merely illustrative. Distinction is notmade between such categories as “workstation,” “server,” “laptop,”“desktop,” “tablet,” “client device,” “mobile device,” “hand-helddevice,” “game console,” “electronic control unit (ECU),” “virtualreality system,” and/or other device or system types, as all arecontemplated within the scope of the computing device of FIG. 5.

The bus 502 may represent one or more busses, such as an address bus, adata bus, a control bus, or a combination thereof. The bus 502 mayinclude one or more bus types, such as an industry standard architecture(ISA) bus, an extended industry standard architecture (EISA) bus, avideo electronics standards association (VESA) bus, a peripheralcomponent interconnect (PCI) bus, a peripheral component interconnectexpress (PCIe) bus, and/or another type of bus.

The memory 504 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 500. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 504 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device500. As used herein, computer storage media does not comprise signalsper se.

The communication media may embody computer-readable instructions, datastructures, program modules, and/or other data types in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” mayrefer to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, the communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, RF, infrared and other wireless media.Combinations of any of the above should also be included within thescope of computer-readable media.

The CPU(s) 506 may be configured to execute the computer-readableinstructions to control one or more components of the computing device500 to perform one or more of the methods and/or processes describedherein. The CPU(s) 506 may each include one or more cores (e.g., one,two, four, eight, twenty-eight, seventy-two, etc.) that are capable ofhandling a multitude of software threads simultaneously. The CPU(s) 506may include any type of processor, and may include different types ofprocessors depending on the type of computing device 500 implemented(e.g., processors with fewer cores for mobile devices and processorswith more cores for servers). For example, depending on the type ofcomputing device 500, the processor may be an ARM processor implementedusing Reduced Instruction Set Computing (RISC) or an x86 processorimplemented using Complex Instruction Set Computing (CISC). Thecomputing device 500 may include one or more CPUs 506 in addition to oneor more microprocessors or supplementary co-processors, such as mathco-processors.

The GPU(s) 508 may be used by the computing device 500 to rendergraphics (e.g., 3D graphics). The GPU(s) 508 may include hundreds orthousands of cores that are capable of handling hundreds or thousands ofsoftware threads simultaneously. The GPU(s) 508 may generate pixel datafor output images in response to rendering commands (e.g., renderingcommands from the CPU(s) 506 received via a host interface). The GPU(s)508 may include graphics memory, such as display memory, for storingpixel data. The display memory may be included as part of the memory504. The GPU(s) 708 may include two or more GPUs operating in parallel(e.g., via a link) When combined together, each GPU 508 may generatepixel data for different portions of an output image or for differentoutput images (e.g., a first GPU for a first image and a second GPU fora second image). Each GPU may include its own memory, or may sharememory with other GPUs.

In examples where the computing device 500 does not include the GPU(s)508, the CPU(s) 506 may be used to render graphics.

The communication interface 510 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 700to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 510 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet),low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or theInternet.

The I/O ports 512 may enable the computing device 500 to be logicallycoupled to other devices including the I/O components 514, thepresentation component(s) 518, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 500.Illustrative I/O components 514 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 514 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 500. Thecomputing device 500 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 500 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 500 to render immersive augmented reality or virtual reality.

The power supply 516 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 516 may providepower to the computing device 500 to enable the components of thecomputing device 500 to operate.

The presentation component(s) 518 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 518 may receivedata from other components (e.g., the GPU(s) 508, the CPU(s) 506, etc.),and output the data (e.g., as an image, video, sound, etc.).

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising: receiving sensor datagenerated using a sensor of a vehicle, the sensor having a sensory fieldto a rear of the vehicle; analyzing the sensor data to determine whetheran object is detected in at least a portion of the sensory field; andbased at least in part on the analyzing the sensor data, determining oneor more of: activation criteria for activating an automatic emergencybraking (AEB) system; activation criteria for activating a collisionmitigation warning (CMW) system; or a braking profile setting for thevehicle once the AEB system is activated.
 2. The method of claim 1,further comprising: receiving additional sensor data generated using oneor more additional sensors of the vehicle, the one or more additionalsensors having associated sensory fields to a front of the vehicle;analyzing the additional sensor data in view of the activation criteria;and upon satisfying the activation criteria, activating one or more ofthe AEB system or the CMW system.
 3. The method of claim 2, wherein theactivating the AEB system is in accordance with the braking profilesetting.
 4. The method of claim 2, wherein the determining the brakingprofile setting is further based at least in part on the analyzing theadditional sensor data.
 5. The method of claim 1, wherein the activationcriteria includes at least a first activation criteria corresponding toa first agreement level between AEB activation determinations and asecond activation criteria corresponding to a second agreement levelbetween the AEB activation determinations, the first agreement level andthe second agreement level being different from one another.
 6. Themethod of claim 5, wherein the first activation criteria is determinedto be the activation criteria when an object is detected in at least theportion of the sensory field and the second activation criteria isdetermined when an object is not detected in at least the portion of thesensory field.
 7. The method of claim 1, wherein the braking profilesetting is representative of a more aggressive braking profile when anobject is not detected in at least the portion of the sensory field thanwhen an object is detected in at least the portion of the sensory field.8. The method of claim 1, further comprising: receiving additionalsensor data generated using another sensor of the vehicle, the anothersensor having another sensory field to a front of the vehicle; andanalyzing the additional sensor data to determine whether an object isdetected in at least a portion of the another sensory field, wherein:determining the braking profile setting is further based at least inpart on the analyzing the additional sensor data; and the brakingprofile setting includes at least one of: a first braking profile whenan object is detected in at least the portion of the sensory field andan object is not detected in at least the portion of the another sensoryfield, a second braking profile when an object is detected in at leastthe portion of the sensory field and an object is detected in at leastthe portion of the another sensory field, and a third braking profilewhen an object is not detected in at least the portion of the sensoryfield and an object is not detected in at least the portion of theanother sensory field.
 9. The method of claim 8, wherein at least theportion of the sensory field and at least the portion of the anotherfield of view or another sensory field corresponding to a lane of travelof the vehicle.
 10. The method of claim 1, wherein the sensor data isfurther analyzed to determine a distance to a detected object, and thedetermining the one or more of the activation criteria or the brakingprofile setting are further based at least in part on the distance. 11.The method of claim 1, wherein the sensor data is further analyzed todetermine whether a detected object is braking, and the determining theone or more of the activation criteria or the braking profile settingare further based at least in part whether the detected object isbraking.
 12. The method of claim 1, wherein the sensor data is furtheranalyzed to determine a past trajectory of a detected object, and thedetermining the one or more of the activation criteria or the brakingprofile setting are further based at least in part on the pasttrajectory.
 13. A method comprising: activating an automatic emergencybraking (AEB) system based at least in part on analyzing first sensordata generated using a first sensor of a vehicle having a first sensoryfield in front of a vehicle; receiving second sensor data generatedusing a second sensor of the vehicle having a second sensory field to arear of the vehicle; analyzing the second sensor data to determinewhether an object is detected in at least a portion of the secondsensory field; and determining a braking profile based at least in parton whether an object is detected in at least the portion of the secondsensory field.
 14. The method of claim 13, further comprising: receivingadditional sensor data generated using one or more additional sensors ofthe vehicle, the one or more additional sensors having associatedsensory fields in front of the vehicle; analyzing the additional sensordata in view of the activation criteria; and upon satisfying theactivation criteria, activating the AEB system.
 15. The method of claim14, wherein the activating the AEB system is in accordance with thebraking profile setting.
 16. The method of claim 14, wherein thedetermining the braking profile setting is further based at least inpart on the analyzing the additional sensor data.
 17. A systemcomprising: at least one sensor having a sensory field to a rear of avehicle; a computing device including one or more processing devices andone or more memory devices communicatively coupled to the one or moreprocessing devices storing programmed instructions thereon, which whenexecuted by the processor causes the instantiation of: an objectdetector to analyze sensor data generated by the at least one sensor anddetermine whether an object is present in at least a portion of thesensory field; and a criteria determiner to determine activationcriteria for activating an automatic emergency braking (AEB) system, theactivation criteria determined based at least in part on whether anobject is detected in at least the portion of the sensory field.
 18. Thesystem of claim 17, further comprising: receiving additional sensor datagenerated using one or more additional sensors of the vehicle, the oneor more additional sensors having associated sensory fields to a frontof the vehicle; analyzing the additional sensor data in view of theactivation criteria; and upon satisfying the activation criteria,activating the AEB system.
 19. The system of claim 18, wherein theactivating the AEB system is in accordance with the braking profilesetting.
 20. The system of claim 18, wherein the determining the brakingprofile setting is further based at least in part on the analyzing theadditional sensor data.